




'''simple docstring'''







import inspect
import tempfile
import unittest

from huggingface_hub import hf_hub_download

from transformers import is_torch_available
from transformers.testing_utils import is_flaky, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


lowerCAmelCase_     						=				1e-4

if is_torch_available():
    import torch

    from transformers import AutoformerConfig, AutoformerForPrediction, AutoformerModel
    from transformers.models.autoformer.modeling_autoformer import AutoformerDecoder, AutoformerEncoder

@require_torch
class 	_snake_case:






 def __init__(self       :  Any    ,		a       :  Tuple    ,		a       :  Any=16    ,		a       :  Optional[Any]=13    ,		a       :  str=7    ,		a       :  List[str]=14    ,		a       :  Optional[int]=10    ,		a       :  List[str]=19    ,		a       :  Dict=5    ,		a       :  int=4    ,		a       :  Tuple=True    ,		a       :  Optional[Any]=16    ,		a       :  Optional[Any]=2    ,		a       :  int=4    ,		a       :  List[str]=4    ,		a       :  Optional[Any]="gelu"    ,		a       :  Any=0.1    ,		a       :  Optional[int]=0.1    ,		a       :  int=[1, 2, 3, 4, 5]    ,		a       :  str=25    ,		a       :  str=5    ,		)       ->   List[Any]:






    """simple docstring"""


    A__       =     d_model
    A__       =     parent
    A__       =     batch_size
    A__       =     prediction_length
    A__       =     context_length
    A__       =     cardinality
    A__       =     num_time_features
    A__       =     lags_sequence
    A__       =     embedding_dimension
    A__       =     is_training
    A__       =     hidden_size
    A__       =     num_hidden_layers
    A__       =     num_attention_heads
    A__       =     intermediate_size
    A__       =     hidden_act
    A__       =     hidden_dropout_prob
    A__       =     attention_probs_dropout_prob

    A__       =     context_length
    A__       =     prediction_length + label_length
    A__       =     label_length

    A__       =     moving_average
    A__       =     autocorrelation_factor






 def 						_UpperCamelCase (self       :  str    )       ->   Optional[int]:






    """simple docstring"""


    return AutoformerConfig(
        d_model=self.d_model    ,		encoder_layers=self.num_hidden_layers    ,		decoder_layers=self.num_hidden_layers    ,		encoder_attention_heads=self.num_attention_heads    ,		decoder_attention_heads=self.num_attention_heads    ,		encoder_ffn_dim=self.intermediate_size    ,		decoder_ffn_dim=self.intermediate_size    ,		dropout=self.hidden_dropout_prob    ,		attention_dropout=self.attention_probs_dropout_prob    ,		prediction_length=self.prediction_length    ,		context_length=self.context_length    ,		label_length=self.label_length    ,		lags_sequence=self.lags_sequence    ,		num_time_features=self.num_time_features    ,		num_static_categorical_features=1    ,		cardinality=[self.cardinality]    ,		embedding_dimension=[self.embedding_dimension]    ,		moving_average=self.moving_average    ,		)






 def 						_UpperCamelCase (self       :  Optional[Any]    ,		a       :  Optional[Any]    )       ->   Dict:






    """simple docstring"""


    A__       =     config.context_length + max(config.lags_sequence    )

    A__       =     ids_tensor([self.batch_size, 1]    ,		config.cardinality[0]    )
    A__       =     floats_tensor([self.batch_size, _past_length, config.num_time_features]    )
    A__       =     floats_tensor([self.batch_size, _past_length]    )
    A__       =     floats_tensor([self.batch_size, _past_length]    ) > 0.5

    # decoder inputs
    A__       =     floats_tensor([self.batch_size, config.prediction_length, config.num_time_features]    )
    A__       =     floats_tensor([self.batch_size, config.prediction_length]    )

    A__       =     {
        'past_values': past_values,
        'static_categorical_features': static_categorical_features,
        'past_time_features': past_time_features,
        'past_observed_mask': past_observed_mask,
        'future_time_features': future_time_features,
        'future_values': future_values,
    }
    return inputs_dict






 def 						_UpperCamelCase (self       :  Union[str, Any]    )       ->   Tuple:






    """simple docstring"""


    A__       =     self.get_config()
    A__       =     self.prepare_autoformer_inputs_dict(a    )
    return config, inputs_dict






 def 						_UpperCamelCase (self       :  List[Any]    )       ->   Union[str, Any]:






    """simple docstring"""


    A__  ,			A__       =     self.prepare_config_and_inputs()
    return config, inputs_dict






 def 						_UpperCamelCase (self       :  List[Any]    ,		a       :  Optional[Any]    ,		a       :  Optional[Any]    )       ->   Tuple:






    """simple docstring"""


    A__       =     AutoformerModel(config=a    ).to(a    ).eval()
    A__       =     model(**a    )

    A__       =     outputs.encoder_last_hidden_state
    A__       =     outputs.last_hidden_state

    with tempfile.TemporaryDirectory() as tmpdirname:
       A__       =     model.get_encoder()
       encoder.save_pretrained(a    )
       A__       =     AutoformerEncoder.from_pretrained(a    ).to(a    )

    A__  ,			A__  ,			A__  ,			A__  ,			A__       =     model.create_network_inputs(**a    )
    A__  ,			A__       =     model.decomposition_layer(transformer_inputs[:, : config.context_length, ...]    )

    A__       =     torch.cat(
        (transformer_inputs[:, : config.context_length, ...], feature[:, : config.context_length, ...])    ,		dim=-1    ,		)
    A__       =     encoder(inputs_embeds=a    )[0]
    self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1e-3    )

    A__       =     (
        torch.mean(transformer_inputs[:, : config.context_length, ...]    ,		dim=1    )
        .unsqueeze(1    )
        .repeat(1    ,		config.prediction_length    ,		1    )
    )
    A__       =     torch.zeros(
        [transformer_inputs.shape[0], config.prediction_length, transformer_inputs.shape[2]]    ,		device=enc_input.device    ,		)

    A__       =     torch.cat(
        (
            torch.cat((seasonal_input[:, -config.label_length :, ...], zeros)    ,		dim=1    ),
            feature[:, config.context_length - config.label_length :, ...],
        )    ,		dim=-1    ,		)
    A__       =     torch.cat(
        (
            torch.cat((trend_input[:, -config.label_length :, ...], mean)    ,		dim=1    ),
            feature[:, config.context_length - config.label_length :, ...],
        )    ,		dim=-1    ,		)

    with tempfile.TemporaryDirectory() as tmpdirname:
       A__       =     model.get_decoder()
       decoder.save_pretrained(a    )
       A__       =     AutoformerDecoder.from_pretrained(a    ).to(a    )

    A__       =     decoder(
        trend=a    ,		inputs_embeds=a    ,		encoder_hidden_states=a    ,		)[0]

    self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1e-3    )




@require_torch
class 	_snake_case(			UpperCAmelCase       , UpperCAmelCase       , unittest.TestCase      ):
 __snake_case:							Dict						=					(AutoformerModel, AutoformerForPrediction) if is_torch_available() else ()
 __snake_case:							List[Any]						=					(AutoformerForPrediction,) if is_torch_available() else ()
 __snake_case:							List[Any]						=					{'''feature-extraction''': AutoformerModel} if is_torch_available() else {}
 __snake_case:							Union[str, Any]						=					False
 __snake_case:							Any						=					False
 __snake_case:							str						=					False
 __snake_case:							str						=					False
 __snake_case:							List[Any]						=					False
 __snake_case:							Optional[Any]						=					False






 def 						_UpperCamelCase (self       :  Dict    )       ->   List[Any]:






    """simple docstring"""


    A__       =     AutoformerModelTester(self    )
    A__       =     ConfigTester(self    ,		config_class=a    ,		has_text_modality=a    )






 def 						_UpperCamelCase (self       :  int    )       ->   Optional[Any]:






    """simple docstring"""


    self.config_tester.run_common_tests()






 def 						_UpperCamelCase (self       :  Union[str, Any]    )       ->   Dict:






    """simple docstring"""


    A__  ,			A__       =     self.model_tester.prepare_config_and_inputs()
    for model_class in self.all_model_classes:
       A__       =     model_class(a    )

       with tempfile.TemporaryDirectory() as tmpdirname:
          model.save_pretrained(a    )
          A__  ,			A__       =     model_class.from_pretrained(a    ,		output_loading_info=a    )
       self.assertEqual(info['missing_keys']    ,		[]    )






 def 						_UpperCamelCase (self       :  Union[str, Any]    )       ->   Optional[int]:






    """simple docstring"""


    A__       =     self.model_tester.prepare_config_and_inputs_for_common()
    self.model_tester.check_encoder_decoder_model_standalone(*a    )






 @unittest.skip(reason='Model has no tokens embeddings'    )
 def 						_UpperCamelCase (self       :  Dict    )       ->   Optional[int]:






    """simple docstring"""


    pass






 def 						_UpperCamelCase (self       :  str    )       ->   List[Any]:






    """simple docstring"""


    A__       =     inspect.signature(getattr(a    ,		'forward'    )    )
    # The main input is the name of the argument after `self`
    A__       =     list(model_signature.parameters.keys()    )[1]
    self.assertEqual(AutoformerModel.main_input_name    ,		a    )






 def 						_UpperCamelCase (self       :  Union[str, Any]    )       ->   int:






    """simple docstring"""


    A__  ,			A__       =     self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
       A__       =     model_class(a    )
       A__       =     inspect.signature(model.forward    )
       # signature.parameters is an OrderedDict => so arg_names order is deterministic
       A__       =     [*signature.parameters.keys()]

       A__       =     [
           'past_values',
           'past_time_features',
           'past_observed_mask',
           'static_categorical_features',
           'static_real_features',
           'future_values',
           'future_time_features',
       ]

       if model.__class__.__name__ in ["AutoformerForPrediction"]:
          expected_arg_names.append('future_observed_mask'    )

       expected_arg_names.extend(
           [
               'decoder_attention_mask',
               'head_mask',
               'decoder_head_mask',
               'cross_attn_head_mask',
               'encoder_outputs',
               'past_key_values',
               'output_hidden_states',
               'output_attentions',
               'use_cache',
               'return_dict',
           ]    )

       self.assertListEqual(arg_names[: len(a    )]    ,		a    )






 def 						_UpperCamelCase (self       :  int    )       ->   List[str]:






    """simple docstring"""


    A__  ,			A__       =     self.model_tester.prepare_config_and_inputs_for_common()
    A__       =     True

    A__       =     getattr(self.model_tester    ,		'seq_length'    ,		a    )
    A__       =     getattr(self.model_tester    ,		'decoder_seq_length'    ,		a    )
    A__       =     getattr(self.model_tester    ,		'encoder_seq_length'    ,		a    )
    A__       =     getattr(self.model_tester    ,		'd_model'    ,		a    )
    A__       =     getattr(self.model_tester    ,		'num_attention_heads'    ,		a    )
    A__       =     d_model // num_attention_heads

    for model_class in self.all_model_classes:
       A__       =     True
       A__       =     False
       A__       =     True
       A__       =     model_class(a    )
       model.to(a    )
       model.eval()
       with torch.no_grad():
          A__       =     model(**self._prepare_for_class(a    ,		a    )    )
       A__       =     outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
       self.assertEqual(len(a    )    ,		self.model_tester.num_hidden_layers    )

       # check that output_attentions also work using config
       del inputs_dict["output_attentions"]
       A__       =     True
       A__       =     model_class(a    )
       model.to(a    )
       model.eval()
       with torch.no_grad():
          A__       =     model(**self._prepare_for_class(a    ,		a    )    )
       A__       =     outputs.encoder_attentions
       self.assertEqual(len(a    )    ,		self.model_tester.num_hidden_layers    )

       self.assertListEqual(
           list(attentions[0].shape[-3:]    )    ,		[self.model_tester.num_attention_heads, encoder_seq_length, dim]    ,		)
       A__       =     len(a    )

       A__       =     7

       if "last_hidden_state" in outputs:
          correct_outlen += 1

       if "trend" in outputs:
          correct_outlen += 1

       if "past_key_values" in outputs:
          correct_outlen += 1  # past_key_values have been returned

       if "loss" in outputs:
          correct_outlen += 1

       if "params" in outputs:
          correct_outlen += 1

       self.assertEqual(a    ,		a    )

       # decoder attentions
       A__       =     outputs.decoder_attentions
       self.assertIsInstance(a    ,		(list, tuple)    )
       self.assertEqual(len(a    )    ,		self.model_tester.num_hidden_layers    )
       self.assertListEqual(
           list(decoder_attentions[0].shape[-3:]    )    ,		[self.model_tester.num_attention_heads, decoder_seq_length, dim]    ,		)

       # cross attentions
       A__       =     outputs.cross_attentions
       self.assertIsInstance(a    ,		(list, tuple)    )
       self.assertEqual(len(a    )    ,		self.model_tester.num_hidden_layers    )
       self.assertListEqual(
           list(cross_attentions[0].shape[-3:]    )    ,		[self.model_tester.num_attention_heads, decoder_seq_length, dim]    ,		)

    # Check attention is always last and order is fine
    A__       =     True
    A__       =     True
    A__       =     model_class(a    )
    model.to(a    )
    model.eval()
    with torch.no_grad():
       A__       =     model(**self._prepare_for_class(a    ,		a    )    )

    self.assertEqual(out_len + 2    ,		len(a    )    )

    A__       =     outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions

    self.assertEqual(len(a    )    ,		self.model_tester.num_hidden_layers    )
    self.assertListEqual(
        list(self_attentions[0].shape[-3:]    )    ,		[self.model_tester.num_attention_heads, encoder_seq_length, dim]    ,		)






 @is_flaky()
 def 						_UpperCamelCase (self       :  Any    )       ->   Dict:






    """simple docstring"""


    super().test_retain_grad_hidden_states_attentions()







def 						_A      ( UpperCAmelCase="train-batch.pt"						):






   '''simple docstring'''







   A__       =     hf_hub_download(repo_id='hf-internal-testing/tourism-monthly-batch'      ,filename=UpperCAmelCase      ,repo_type='dataset'						)
   A__       =     torch.load(UpperCAmelCase      ,map_location=UpperCAmelCase						)
   return batch







@require_torch
@slow
class 	_snake_case(			unittest.TestCase      ):






 def 						_UpperCamelCase (self       :  List[Any]    )       ->   int:






    """simple docstring"""


    A__       =     AutoformerModel.from_pretrained('huggingface/autoformer-tourism-monthly'    ).to(a    )
    A__       =     prepare_batch()

    with torch.no_grad():
       A__       =     model(
           past_values=batch['past_values']    ,		past_time_features=batch['past_time_features']    ,		past_observed_mask=batch['past_observed_mask']    ,		static_categorical_features=batch['static_categorical_features']    ,		future_values=batch['future_values']    ,		future_time_features=batch['future_time_features']    ,		)[0]

    A__       =     torch.Size(
        (64, model.config.prediction_length + model.config.label_length, model.config.feature_size)    )
    self.assertEqual(output.shape    ,		a    )

    A__       =     torch.tensor(
        [[0.3593, -1.3398, 0.6330], [0.2279, 1.5396, -0.1792], [0.0450, 1.3225, -0.2335]]    ,		device=a    )
    self.assertTrue(torch.allclose(output[0, :3, :3]    ,		a    ,		atol=a    )    )






 def 						_UpperCamelCase (self       :  Dict    )       ->   Optional[int]:






    """simple docstring"""


    A__       =     AutoformerForPrediction.from_pretrained('huggingface/autoformer-tourism-monthly'    ).to(a    )
    A__       =     prepare_batch('val-batch.pt'    )
    with torch.no_grad():
       A__       =     model(
           past_values=batch['past_values']    ,		past_time_features=batch['past_time_features']    ,		past_observed_mask=batch['past_observed_mask']    ,		static_categorical_features=batch['static_categorical_features']    ,		).encoder_last_hidden_state
    A__       =     torch.Size((64, model.config.context_length, model.config.d_model)    )
    self.assertEqual(output.shape    ,		a    )

    A__       =     torch.tensor(
        [[-0.0734, -0.9036, 0.8358], [4.7186, 2.4113, 1.9581], [1.7953, 2.3558, 1.2970]]    ,		device=a    )
    self.assertTrue(torch.allclose(output[0, :3, :3]    ,		a    ,		atol=a    )    )






 def 						_UpperCamelCase (self       :  Union[str, Any]    )       ->   Tuple:






    """simple docstring"""


    A__       =     AutoformerForPrediction.from_pretrained('huggingface/autoformer-tourism-monthly'    ).to(a    )
    A__       =     prepare_batch('val-batch.pt'    )
    with torch.no_grad():
       A__       =     model.generate(
           static_categorical_features=batch['static_categorical_features']    ,		past_time_features=batch['past_time_features']    ,		past_values=batch['past_values']    ,		future_time_features=batch['future_time_features']    ,		past_observed_mask=batch['past_observed_mask']    ,		)
    A__       =     torch.Size((64, model.config.num_parallel_samples, model.config.prediction_length)    )
    self.assertEqual(outputs.sequences.shape    ,		a    )

    A__       =     torch.tensor([3130.6763, 4056.5293, 7053.0786]    ,		device=a    )
    A__       =     outputs.sequences.mean(dim=1    )
    self.assertTrue(torch.allclose(mean_prediction[0, -3:]    ,		a    ,		rtol=1e-1    )    )








'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


lowerCAmelCase_     						=				{}

try:
    if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase_     						=				['''GPTSw3Tokenizer''']


if TYPE_CHECKING:
    try:
        if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_gpt_swa import GPTSwaTokenizer

else:
    import sys

    lowerCAmelCase_     						=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


